Transitioning From Community-Based to Institutional Long-term Care: Comparing 1915(c) Waiver and PACE Enrollees

Background While existing reviews have identified significant predictors of nursing home admission, this meta-analysis attempted to provide more integrated empirical findings to identify predictors. The present study aimed to generate pooled empirical associations for sociodemographic, functional, cognitive, service use, and informal support indicators that predict nursing home admission among older adults in the U.S. Methods Studies published in English were retrieved by searching the MEDLINE, PSYCINFO, CINAHL, and Digital Dissertations databases using the keywords: "nursing home placement," "nursing home entry," "nursing home admission," and "predictors/institutionalization." Any reports including these key words were retrieved. Bibliographies of retrieved articles were also searched. Selected studies included sampling frames that were nationally- or regionally-representative of the U.S. older population. Results Of 736 relevant reports identified, 77 reports across 12 data sources were included that used longitudinal designs and community-based samples. Information on number of nursing home admissions, length of follow-up, sample characteristics, analysis type, statistical adjustment, and potential risk factors were extracted with standardized protocols. Random effects models were used to separately pool the logistic and Cox regression model results from the individual data sources. Among the strongest predictors of nursing home admission were 3 or more activities of daily living dependencies (summary odds ratio [OR] = 3.25; 95% confidence interval [CI], 2.56–4.09), cognitive impairment (OR = 2.54; CI, 1.44–4.51), and prior nursing home use (OR = 3.47; CI, 1.89–6.37). Conclusion The pooled associations provided detailed empirical information as to which variables emerged as the strongest predictors of NH admission (e.g., 3 or more ADL dependencies, cognitive impairment, prior NH use). These results could be utilized as weights in the construction and validation of prognostic tools to estimate risk for NH entry over a multi-year period.

Long-term care resources would be allocated more cost-effectively if care planning and medical/functional eligibility decisions were grounded more firmly in extant evidence regarding the risk of nursing home placement, hospitalization, functional impairment, and mortality. This article synthesizes the studies that longitudinally assess the predictors of each of these outcomes for the 65 and older population in the United States. A database was assembled containing 167 multivariate analyses abstracted from 78 journal articles published between 1985 and 1998. Findings show that 22 risk factors consistently predict two or more outcomes, including three that predict all four: worse performance on physical function measures not based on activities of daily living, greater illness severity, and prior hospital use. Findings should help prioritize variable selection choices of those setting eligibility criteria, allocating care resources, and doing descriptive studies. Gaps are shown to exist in the understanding of outcome effects of facility, market, policy, and other system attributes.